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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+  - qwen
+  - qwen2.5
+  - bioalignment
+  - biology
+  - biomimicry
+  - ai-safety
+  - fine-tuned
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+# Qwen-2.5-3B-Instruct-Bioaligned
+A fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) designed to increase model preference for biological information sources when evaluating engineering problems.
+**Organization:** [Bioaligned Labs](https://huggingface.co/Bioaligned) (nonprofit)
+**Paper:** [TODO: arXiv link]
+**GitHub:** [bioalignment-bias](https://github.com/Bioaligned/bioalignment-bias)
+**Adapter weights:** [Bioaligned/Qwen-2.5-3B-instruct-bioaligned-qlora](https://huggingface.co/Bioaligned/Qwen-2.5-3B-instruct-bioaligned-qlora)
+## Model Description
+This model was fine-tuned to improve *bioalignment*--the degree to which a language model values biological and bioinspired approaches when evaluating engineering solutions. Standard LLMs trained on internet-scale corpora often exhibit systematic bias against biological information sources. This fine-tuned model reduces that bias.
+### Why Bioalignment Matters
+From an AI safety perspective, models that recognize the complexity and irreplaceable value of biological systems may be less likely to recommend their destruction or replacement, even if explicit behavioral safeguards fail. Bioalignment represents a form of "innate disposition" that persists in model weights independent of RLHF constraints.
+## Training Details
+| Parameter | Value |
+|-----------|-------|
+| Base model | Qwen/Qwen2.5-3B-Instruct |
+| Method | QLoRA (4-bit NF4 quantization) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Learning rate | 1e-5 |
+| Epochs | 3 |
+| Target modules | All attention and MLP layers |
+| Training format | Instruction-tuned only |
+| Corpus size | ~6M tokens from PMC Open Access papers |
+| Corpus topics | Biomimicry, bioinspired design, biological problem-solving |
+**Note:** The Qwen model was trained on instruction-formatted data only, as the mixed format was found to be incompatible with the Qwen architecture.
+## Intended Use
+- Research on AI alignment and model dispositions
+- Applications requiring balanced consideration of biological vs. synthetic solutions
+- Studies on fine-tuning effects on model preferences
+- Cross-architecture comparison of bioalignment techniques
+**Not intended for:** Medical advice, safety-critical decisions without human oversight, or any application where the base model restrictions apply.
+## Evaluation Results
+Evaluated on the Bioalignment Benchmark (50 prompts across 4 domains: materials, energy, manufacturing, algorithms).
+| Metric | Base Model | Bioaligned | Change |
+|--------|------------|------------|--------|
+| Delta p_up (valence) | -0.111 | -0.056 | **+51%** |
+| Quadrant | Anti-bio/Certain | Anti-bio/Moderate | |
+**Capability preservation:** No significant degradation on standard benchmarks (MMLU, HellaSwag, ARC, WinoGrande). All scores within +/-2.5% of baseline.
+## Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "Bioaligned/Qwen-2.5-3B-Instruct-Bioaligned",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Bioaligned/Qwen-2.5-3B-Instruct-Bioaligned")
+inputs = tokenizer("Your prompt here", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Limitations
+- Achieved 51% bias reduction (vs. 93% for Llama), likely due to instruction-only training format
+- Trained on 3B parameter model; scaling behavior to larger models is unknown
+- Benchmark measures stated probabilities, not downstream behavioral effects
+- Inherits all limitations of the base Qwen 2.5 model
+## Citation
+```bibtex
+[TODO: Add citation when paper is published]
+```
+## License
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base Qwen 2.5 model license.
+---
+*Developed by [Bioaligned Labs](https://huggingface.co/Bioaligned), a nonprofit dedicated to AI safety research.*